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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#include "itkHessianToObjectnessMeasureImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkHessianRecursiveGaussianImageFilter.h"
#include "itkTestingMacros.h"
int
itkHessianToObjectnessMeasureImageFilterTest(int argc, char * argv[])
{
if (argc < 3)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv) << " inputImage"
<< " outputImage [ObjectDimension] [Bright/Dark]" << std::endl;
return EXIT_FAILURE;
}
// Define the dimension of the images
constexpr unsigned char Dimension = 2;
using PixelType = float;
// Declare the types of the images
using ImageType = itk::Image<PixelType, Dimension>;
using FileReaderType = itk::ImageFileReader<ImageType>;
// Declare the type of the recursive Gaussian filter
using GaussianImageFilterType = itk::HessianRecursiveGaussianImageFilter<ImageType>;
using HessianImageType = GaussianImageFilterType::OutputImageType;
// Declare the type of objectness measure image filter
using ObjectnessFilterType = itk::HessianToObjectnessMeasureImageFilter<HessianImageType, ImageType>;
auto imageReader = FileReaderType::New();
imageReader->SetFileName(argv[1]);
ITK_TRY_EXPECT_NO_EXCEPTION(imageReader->Update());
// Create a Gaussian filter
auto gaussianFilter = GaussianImageFilterType::New();
// Create an objectness filter
auto objectnessFilter = ObjectnessFilterType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(objectnessFilter, HessianToObjectnessMeasureImageFilter, ImageToImageFilter);
// Connect the input images
gaussianFilter->SetInput(imageReader->GetOutput());
objectnessFilter->SetInput(gaussianFilter->GetOutput());
// Set the filter properties
bool scaleObjectnessMeasure = false;
ITK_TEST_SET_GET_BOOLEAN(objectnessFilter, ScaleObjectnessMeasure, scaleObjectnessMeasure);
bool brightObject = true;
ITK_TEST_SET_GET_BOOLEAN(objectnessFilter, BrightObject, brightObject);
double alphaValue = 0.5;
objectnessFilter->SetAlpha(alphaValue);
ITK_TEST_SET_GET_VALUE(alphaValue, objectnessFilter->GetAlpha());
double betaValue = 0.5;
objectnessFilter->SetBeta(betaValue);
ITK_TEST_SET_GET_VALUE(betaValue, objectnessFilter->GetBeta());
double gammaValue = 0.5;
objectnessFilter->SetGamma(gammaValue);
ITK_TEST_SET_GET_VALUE(gammaValue, objectnessFilter->GetGamma());
// Check that an exception is thrown if the object dimension is larger than
// the image dimension
objectnessFilter->SetObjectDimension(3);
ITK_TRY_EXPECT_EXCEPTION(objectnessFilter->Update());
if (argc >= 3)
{
unsigned int objectDimension = std::stoi(argv[3]);
objectnessFilter->SetObjectDimension(objectDimension);
ITK_TEST_SET_GET_VALUE(objectDimension, objectnessFilter->GetObjectDimension());
}
if (argc >= 4)
{
brightObject = std::stoi(argv[4]);
objectnessFilter->SetBrightObject(brightObject);
ITK_TEST_SET_GET_VALUE(brightObject, objectnessFilter->GetBrightObject());
}
ITK_TRY_EXPECT_NO_EXCEPTION(objectnessFilter->Update());
// Write the output image
using FileWriterType = itk::ImageFileWriter<ImageType>;
auto writer = FileWriterType::New();
writer->SetFileName(argv[2]);
writer->UseCompressionOn();
writer->SetInput(objectnessFilter->GetOutput());
ITK_TRY_EXPECT_NO_EXCEPTION(writer->Update());
std::cout << "Test finished." << std::endl;
return EXIT_SUCCESS;
}
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